Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 59469

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 65191

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-14T20:22:23+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`)
beds_by_pref
}

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	118845
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	117108	0
dateAnnounced	0	1.00	10	10	0	291	0
gender	23135	0.81	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	114812	0.03	8	23	0	8	0
notes	61963	0.48	1	270	0	54084	1
mhlwPatientNumber	118396	0.00	1	11	0	434	0
prefecturePatientNumber	20439	0.83	5	20	0	98397	0
prefectureSourceURL	87524	0.26	5	224	0	3442	0
residence	31266	0.74	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8440	0
relatedPatients	107995	0.09	2	259	0	6553	0
knownCluster	116345	0.02	3	88	0	231	0
detectedCityTown	92234	0.22	2	22	0	663	0
cityPrefectureNumber	92491	0.22	1	34	0	26345	2
citySourceURL	106932	0.10	9	317	0	3656	0
deceasedDate	116971	0.02	10	10	0	240	0
deceasedReportedDate	117625	0.01	10	62	0	207	0
deathSourceURL	117770	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 117107, FAL: 1738
charterFlightPassenger	118831	0	1.00	TRU: 14
cruisePassengerDisembarked	118834	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	30.38	24.24	-1	10	30	50	100	▆▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	115757
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	115757	0
knownCluster	113307	0.02	3	88	0	228	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-14	2020-08-19	288
deceasedDate	115379	0	2020-02-13	2020-10-17	2020-05-08	149
deceasedReportedDate	115428	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	21629	0.81	FALSE	2	M: 52778, F: 41350
patientStatus	113247	0.02	FALSE	8	Hos: 1246, Dec: 370, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	20: 25670, -1: 21716, 30: 16313, 40: 13609
pcode	0	1.00	FALSE	47	13: 34534, 27: 15155, 14: 10059, 23: 7552
pref	0	1.00	FALSE	47	東京都: 34534, 大阪府: 15155, 神奈川: 10059, 愛知県: 7552
region	0	1.00	FALSE	8	関東地: 59469, 近畿地: 23750, 中部地: 11689, 九州地: 11578
広域圏	8974	0.92	FALSE	8	首都圏: 59750, 近畿圏: 23135, 中部圏: 10272, 九州圏: 7878
通俗的区分	0	1.00	FALSE	11	関東: 59469, 関西: 23135, 東海: 9825, 九州: 7878
fct_pref	0	1.00	FALSE	47	Tok: 34534, Osa: 15155, Kan: 10059, Aic: 7552

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 115757
charterFlightPassenger	115750	0	1.00	TRU: 7
cruisePassengerDisembarked	115746	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 113307, TRU: 2450

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.92）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.92）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

　

地方別

陽性者数（単日）

　

移動平均

　

累積

　

単日＋累積

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 254.8248 262.0886 273.1412 280.9103 293.7981 300.6759 309.5473 323.4943
##  [9] 335.6418 348.3480 358.9761 368.8716 378.0070 387.6479
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 244.7134 239.3608
## 44.71429 251.1330 245.3334
## 44.85714 259.9008 252.8917
## 45.00000 266.5339 258.9234
## 45.14286 277.5476 268.9451
## 45.28571 282.5156 272.9020
## 45.42857 288.8577 277.9053
## 45.57143 299.5850 286.9282
## 45.71429 308.7903 294.5760
## 45.85714 318.5708 302.8076
## 46.00000 326.5186 309.3366
## 46.14286 333.7556 315.1664
## 46.28571 340.1824 320.1593
## 46.42857 346.9102 325.3449
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 264.9362 270.2889
## 44.71429 273.0443 278.8439
## 44.85714 286.3816 293.3907
## 45.00000 295.2868 302.8972
## 45.14286 310.0486 318.6511
## 45.28571 318.8363 328.4498
## 45.42857 330.2369 341.1893
## 45.57143 347.4035 360.0603
## 45.71429 362.4933 376.7076
## 45.85714 378.1252 393.8883
## 46.00000 391.4336 408.6156
## 46.14286 403.9875 422.5768
## 46.28571 415.8316 435.8547
## 46.42857 428.3857 449.9509
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 31.41114 27.58581 31.84252 33.89309 31.06208 35.13813 32.12298 33.68522
##  [9] 35.22208 32.52729 30.03586 32.24083 29.94645 34.12175
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 24.46078 20.78148
## 44.71429 20.48526 16.72646
## 44.85714 24.59490 20.75825
## 45.00000 26.50132 22.58836
## 45.14286 23.52892 19.54111
## 45.28571 27.46619 23.40490
## 45.42857 24.31472 20.18128
## 45.57143 25.57378 21.27985
## 45.71429 26.95276 22.57524
## 45.85714 24.10304 19.64350
## 46.00000 21.45947 16.91940
## 46.14286 23.51496 18.89576
## 46.28571 21.07361 16.37662
## 46.42857 25.10435 20.33082
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 38.36150 42.04080
## 44.71429 34.68636 38.44516
## 44.85714 39.09014 42.92680
## 45.00000 41.28486 45.19782
## 45.14286 38.59523 42.58305
## 45.28571 42.81007 46.87135
## 45.42857 39.93124 44.06469
## 45.57143 41.79665 46.09059
## 45.71429 43.49141 47.86893
## 45.85714 40.95155 45.41108
## 46.00000 38.61225 43.15232
## 46.14286 40.96670 45.58590
## 46.28571 38.81928 43.51628
## 46.42857 43.13915 47.91268
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 575.2759 469.3938 642.9523 672.8068 773.7155 750.9139 767.0634 600.1691
##  [9] 496.7564 656.0453 686.6527 781.1347 765.7506 781.1885
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 511.9828 478.4775
## 44.71429 389.7634 347.6096
## 44.85714 554.9726 508.3990
## 45.00000 581.5685 533.2699
## 45.14286 678.9334 628.7588
## 45.28571 652.0064 599.6480
## 45.42857 661.2962 605.3065
## 45.57143 481.6345 418.8860
## 45.71429 367.7659 299.4825
## 45.85714 520.3718 448.5507
## 46.00000 546.4540 472.2373
## 46.14286 637.5174 561.4910
## 46.28571 618.4813 540.5216
## 46.42857 629.7729 549.6183
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 638.5689  672.0743
## 44.71429 549.0243  591.1781
## 44.85714 730.9319  777.5055
## 45.00000 764.0451  812.3438
## 45.14286 868.4975  918.6721
## 45.28571 849.8213  902.1797
## 45.42857 872.8306  928.8204
## 45.57143 718.7038  781.4523
## 45.71429 625.7468  694.0302
## 45.85714 791.7187  863.5399
## 46.00000 826.8514  901.0682
## 46.14286 924.7520 1000.7785
## 46.28571 913.0200  990.9796
## 46.42857 932.6042 1012.7588
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 195.1359 179.6695 213.6430 219.7108 219.8341 239.8115 249.1148 223.5446
##  [9] 231.1028 245.9217 253.4515 268.2327 253.7810 271.7239
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 176.8378 167.1514
## 44.71429 156.8470 144.7654
## 44.85714 186.7934 172.5801
## 45.00000 188.7156 172.3077
## 45.14286 186.2230 168.4304
## 45.28571 202.8161 183.2319
## 45.42857 209.5167 188.5548
## 45.57143 178.4265 154.5425
## 45.71429 181.1822 154.7559
## 45.85714 192.4066 164.0775
## 46.00000 195.7168 165.1538
## 46.14286 207.1132 174.7586
## 46.28571 189.4037 155.3244
## 46.42857 203.8534 167.9249
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 213.4339 223.1203
## 44.71429 202.4921 214.5736
## 44.85714 240.4925 254.7058
## 45.00000 250.7061 267.1140
## 45.14286 253.4452 271.2378
## 45.28571 276.8070 296.3912
## 45.42857 288.7128 309.6748
## 45.57143 268.6627 292.5468
## 45.71429 281.0235 307.4498
## 45.85714 299.4367 327.7659
## 46.00000 311.1863 341.7492
## 46.14286 329.3522 361.7069
## 46.28571 318.1583 352.2376
## 46.42857 339.5944 375.5229
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 355.3077 296.6564 432.5759 442.6962 446.5056 477.6230 497.3133 427.7629
##  [9] 365.1039 501.6704 508.9082 512.5944 541.5139 560.6456
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 321.9583 304.3042
## 44.71429 257.2037 236.3186
## 44.85714 390.1369 367.6710
## 45.00000 396.1178 371.4607
## 45.14286 397.3043 371.2587
## 45.28571 425.2935 397.5920
## 45.42857 442.6741 413.7499
## 45.57143 363.6958 329.7807
## 45.71429 295.9819 259.3910
## 45.85714 428.5928 389.9078
## 46.00000 431.8091 390.9953
## 46.14286 432.1157 389.5129
## 46.28571 457.7008 413.3328
## 46.42857 473.9045 427.9865
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 388.6572 406.3113
## 44.71429 336.1092 356.9942
## 44.85714 475.0150 497.4809
## 45.00000 489.2746 513.9317
## 45.14286 495.7069 521.7525
## 45.28571 529.9524 557.6539
## 45.42857 551.9524 580.8766
## 45.57143 491.8301 525.7452
## 45.71429 434.2258 470.8167
## 45.85714 574.7481 613.4331
## 46.00000 586.0074 626.8212
## 46.14286 593.0731 635.6759
## 46.28571 625.3271 669.6951
## 46.42857 647.3867 693.3046
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 18.51958 17.39923 17.39923 17.39923 17.39923 17.39923 17.39923 17.39923
##  [9] 17.39923 17.39923 17.39923 17.39923 17.39923 17.39923
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%         95%
## 44.57143 9.419249  4.60182352
## 44.71429 7.856590  2.80502145
## 44.85714 7.664932  2.51190578
## 45.00000 7.476976  2.22445087
## 45.14286 7.292514  1.94234089
## 45.28571 7.111359  1.66528834
## 45.42857 6.933339  1.39303059
## 45.57143 6.758297  1.12532700
## 45.71429 6.586088  0.86195640
## 45.85714 6.416579  0.60271496
## 46.00000 6.249647  0.34741435
## 46.14286 6.085178  0.09588015
## 46.28571 5.923065 -0.15204960
## 46.42857 5.763211 -0.39652553
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 27.61991 32.43734
## 44.71429 26.94187 31.99344
## 44.85714 27.13353 32.28656
## 45.00000 27.32149 32.57401
## 45.14286 27.50595 32.85612
## 45.28571 27.68710 33.13317
## 45.42857 27.86512 33.40543
## 45.57143 28.04016 33.67313
## 45.71429 28.21237 33.93651
## 45.85714 28.38188 34.19575
## 46.00000 28.54881 34.45105
## 46.14286 28.71328 34.70258
## 46.28571 28.87540 34.95051
## 46.42857 29.03525 35.19499
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 2.032099 2.294748 2.294748 2.294748 2.294748 2.294748 2.294748 2.294748
##  [9] 2.294748 2.294748 2.294748 2.294748 2.294748 2.294748
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.444687 -3.285186
## 44.71429 -1.434803 -3.409108
## 44.85714 -1.506238 -3.518358
## 45.00000 -1.576355 -3.625593
## 45.14286 -1.645224 -3.730920
## 45.28571 -1.712910 -3.834437
## 45.42857 -1.779472 -3.936234
## 45.57143 -1.844964 -4.036395
## 45.71429 -1.909436 -4.134996
## 45.85714 -1.972933 -4.232107
## 46.00000 -2.035500 -4.327795
## 46.14286 -2.097176 -4.422120
## 46.28571 -2.157997 -4.515138
## 46.42857 -2.217999 -4.606902
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 5.508886 7.349385
## 44.71429 6.024299 7.998604
## 44.85714 6.095734 8.107855
## 45.00000 6.165851 8.215090
## 45.14286 6.234721 8.320416
## 45.28571 6.302407 8.423933
## 45.42857 6.368969 8.525731
## 45.57143 6.434460 8.625892
## 45.71429 6.498932 8.724493
## 45.85714 6.562430 8.821604
## 46.00000 6.624997 8.917292
## 46.14286 6.686672 9.011616
## 46.28571 6.747493 9.104634
## 46.42857 6.807495 9.196399
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 65.53264 50.54461 60.07731 71.46050 63.25567 76.52559 61.50166 68.31522
##  [9] 59.80775 66.07616 72.32263 67.31602 76.60494 66.68795
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%         95%
## 44.57143 43.5414718  31.9000505
## 44.71429 23.9056447   9.8038271
## 44.85714 29.9616007  14.0193063
## 45.00000 40.0105214  23.3619094
## 45.14286 29.7536713  12.0187840
## 45.28571 39.1214701  19.3209245
## 45.42857 21.5050986   0.3322002
## 45.57143 22.7993077  -1.2953582
## 45.71429 10.4170965 -15.7287338
## 45.85714 12.3673684 -16.0643505
## 46.00000 15.4492021 -14.6577697
## 46.14286  7.6476117 -23.9389337
## 46.28571 13.6206744 -19.7211798
## 46.42857  0.5241616 -34.5008331
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143  87.52381  99.16523
## 44.71429  77.18358  91.28540
## 44.85714  90.19302 106.13532
## 45.00000 102.91047 119.55909
## 45.14286  96.75767 114.49256
## 45.28571 113.92971 133.73026
## 45.42857 101.49821 122.67111
## 45.57143 113.83112 137.92579
## 45.71429 109.19839 135.34422
## 45.85714 119.78496 148.21668
## 46.00000 129.19605 159.30302
## 46.14286 126.98442 158.57097
## 46.28571 139.58920 172.93105
## 46.42857 132.85174 167.87674

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 254.8248 262.0886 273.1412 280.9103 293.7981 300.6759 309.5473 323.4943
##  [9] 335.6418 348.3480 358.9761 368.8716 378.0070 387.6479
## 
## $北海道$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 244.7134 239.3608
## 44.71429 251.1330 245.3334
## 44.85714 259.9008 252.8917
## 45.00000 266.5339 258.9234
## 45.14286 277.5476 268.9451
## 45.28571 282.5156 272.9020
## 45.42857 288.8577 277.9053
## 45.57143 299.5850 286.9282
## 45.71429 308.7903 294.5760
## 45.85714 318.5708 302.8076
## 46.00000 326.5186 309.3366
## 46.14286 333.7556 315.1664
## 46.28571 340.1824 320.1593
## 46.42857 346.9102 325.3449
## 
## $北海道$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 264.9362 270.2889
## 44.71429 273.0443 278.8439
## 44.85714 286.3816 293.3907
## 45.00000 295.2868 302.8972
## 45.14286 310.0486 318.6511
## 45.28571 318.8363 328.4498
## 45.42857 330.2369 341.1893
## 45.57143 347.4035 360.0603
## 45.71429 362.4933 376.7076
## 45.85714 378.1252 393.8883
## 46.00000 391.4336 408.6156
## 46.14286 403.9875 422.5768
## 46.28571 415.8316 435.8547
## 46.42857 428.3857 449.9509
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 2.642929 1.213991 2.047385 1.561328 1.844809 1.679475 1.775902 1.719663
##  [9] 1.752463 1.733333 1.744490 1.737983 1.741778 1.739565
## 
## $青森県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%       95%
## 44.57143  0.09250551 -1.257607
## 44.71429 -1.78544692 -3.373253
## 44.85714 -1.02871725 -2.657107
## 45.00000 -1.74241489 -3.491310
## 45.14286 -1.57940745 -3.392078
## 45.28571 -1.91044284 -3.810831
## 45.42857 -1.94390380 -3.913050
## 45.57143 -2.14141203 -4.185342
## 45.71429 -2.23590712 -4.347223
## 45.85714 -2.38344179 -4.562731
## 46.00000 -2.49393447 -4.737621
## 46.14286 -2.62032015 -4.927467
## 46.28571 -2.73226951 -5.100687
## 46.42857 -2.84783279 -5.276255
## 
## $青森県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 5.193352 6.543465
## 44.71429 4.213428 5.801234
## 44.85714 5.123487 6.751877
## 45.00000 4.865070 6.613965
## 45.14286 5.269025 7.081696
## 45.28571 5.269393 7.169781
## 45.42857 5.495708 7.464854
## 45.57143 5.580739 7.624669
## 45.71429 5.740834 7.852149
## 45.85714 5.850109 8.029398
## 46.00000 5.982915 8.226602
## 46.14286 6.096287 8.403434
## 46.28571 6.215826 8.584244
## 46.42857 6.326963 8.755385
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1]  0.4349745  0.9987295  0.9529954  3.6574157  4.4534504  5.0895442
##  [7]  0.8843918  1.0190844  0.9299195  0.9023636 -0.1627755 -0.4960252
## [13] -0.8852521  1.1675909
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.7663074 -1.402228
## 44.71429 -0.3698182 -1.094284
## 44.85714 -0.4648548 -1.215419
## 45.00000  2.2223212  1.462628
## 45.14286  3.0111424  2.247631
## 45.28571  3.6435295  2.878056
## 45.42857 -0.5639611 -1.330673
## 45.57143 -0.5129176 -1.323911
## 45.71429 -0.6309991 -1.457300
## 45.85714 -0.6706235 -1.503313
## 46.00000 -1.7419678 -2.577942
## 46.14286 -2.0791370 -2.917186
## 46.28571 -2.4712981 -3.310900
## 46.42857 -0.4209306 -1.261843
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143 1.6362565 2.272177
## 44.71429 2.3672772 3.091743
## 44.85714 2.3708456 3.121410
## 45.00000 5.0925102 5.852203
## 45.14286 5.8957584 6.659270
## 45.28571 6.5355589 7.301033
## 45.42857 2.3327448 3.099457
## 45.57143 2.5510865 3.362079
## 45.71429 2.4908382 3.317139
## 45.85714 2.4753507 3.308040
## 46.00000 1.4164169 2.252391
## 46.14286 1.0870865 1.925135
## 46.28571 0.7007938 1.540396
## 46.42857 2.7561124 3.597025
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 23.33331 16.90906 21.99768 17.96701 21.15968 18.63078 20.63391 19.04724
##  [9] 20.30404 19.30853 20.09707 19.47247 19.96721 19.57533
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 18.51922 15.970792
## 44.71429 12.05883  9.491272
## 44.85714 16.76868 14.000620
## 45.00000 12.66542  9.858922
## 45.14286 15.58314 12.631094
## 45.28571 12.95585  9.951722
## 45.42857 14.74082 11.621201
## 45.57143 13.04035  9.860497
## 45.71429 14.11263 10.835093
## 45.85714 12.99542  9.653454
## 46.00000 13.62072 10.192354
## 46.14286 12.87132  9.376884
## 46.28571 13.21714  9.643865
## 46.42857 12.70025  9.060796
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 28.14740 30.69582
## 44.71429 21.75930 24.32686
## 44.85714 27.22667 29.99473
## 45.00000 23.26860 26.07510
## 45.14286 26.73623 29.68827
## 45.28571 24.30571 27.30984
## 45.42857 26.52701 29.64662
## 45.57143 25.05413 28.23399
## 45.71429 26.49545 29.77298
## 45.85714 25.62165 28.96361
## 46.00000 26.57341 30.00178
## 46.14286 26.07363 29.56806
## 46.28571 26.71728 30.29056
## 46.42857 26.45042 30.08987
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.6351395 0.4939466 0.4939466 0.4939466 0.4939466 0.4939466 0.4939466
##  [8] 0.4939466 0.4939466 0.4939466 0.4939466 0.4939466 0.4939466 0.4939466
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.6027823 -1.258099
## 44.71429 -0.7558135 -1.417397
## 44.85714 -0.7575135 -1.419997
## 45.00000 -0.7592113 -1.422593
## 45.14286 -0.7609067 -1.425186
## 45.28571 -0.7625999 -1.427775
## 45.42857 -0.7642908 -1.430361
## 45.57143 -0.7659794 -1.432944
## 45.71429 -0.7676657 -1.435523
## 45.85714 -0.7693498 -1.438099
## 46.00000 -0.7710317 -1.440671
## 46.14286 -0.7727113 -1.443239
## 46.28571 -0.7743887 -1.445805
## 46.42857 -0.7760639 -1.448367
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.873061 2.528378
## 44.71429 1.743707 2.405290
## 44.85714 1.745407 2.407890
## 45.00000 1.747104 2.410486
## 45.14286 1.748800 2.413079
## 45.28571 1.750493 2.415669
## 45.42857 1.752184 2.418255
## 45.57143 1.753873 2.420837
## 45.71429 1.755559 2.423416
## 45.85714 1.757243 2.425992
## 46.00000 1.758925 2.428564
## 46.14286 1.760604 2.431133
## 46.28571 1.762282 2.433698
## 46.42857 1.763957 2.436260
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.4212727 1.4607918 1.5035958 1.0916992 1.2705468 1.6402325 1.1731829
##  [8] 1.1795698 1.0888866 0.9983373 0.9095310 0.8238054 0.7422431 0.6656903
## 
## $山形県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%         95%
## 44.57143  0.47401035 -0.02744003
## 44.71429  0.49451302 -0.01700406
## 44.85714  0.50883712 -0.01775632
## 45.00000  0.06072866 -0.48503416
## 45.14286  0.19810938 -0.36960471
## 45.28571  0.52365125 -0.06743116
## 45.42857  0.01207283 -0.60258169
## 45.57143  0.01169972 -0.60653334
## 45.71429 -0.11094537 -0.74609806
## 45.85714 -0.22845462 -0.87787905
## 46.00000 -0.33951725 -1.00072346
## 46.14286 -0.44321038 -1.11392802
## 46.28571 -0.53893596 -1.21715121
## 46.42857 -0.62636493 -1.31033767
## 
## $山形県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 2.368535 2.869985
## 44.71429 2.427071 2.938588
## 44.85714 2.498354 3.024948
## 45.00000 2.122670 2.668433
## 45.14286 2.342984 2.910698
## 45.28571 2.756814 3.347896
## 45.42857 2.334293 2.948947
## 45.57143 2.347440 2.965673
## 45.71429 2.288719 2.923871
## 45.85714 2.225129 2.874554
## 46.00000 2.158579 2.819786
## 46.14286 2.090821 2.761539
## 46.28571 2.023422 2.701637
## 46.42857 1.957746 2.641718
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 3.045868 3.045868 3.045868 3.045868 3.045868 3.045868 3.045868 3.045868
##  [9] 3.045868 3.045868 3.045868 3.045868 3.045868 3.045868
## 
## $福島県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%        95%
## 44.57143  0.49896934 -0.8492776
## 44.71429  0.43591321 -0.9457137
## 44.85714  0.37434499 -1.0398741
## 45.00000  0.31416406 -1.1319129
## 45.14286  0.25528068 -1.2219673
## 45.28571  0.19761436 -1.3101603
## 45.42857  0.14109262 -1.3966028
## 45.57143  0.08564990 -1.4813952
## 45.71429  0.03122666 -1.5646283
## 45.85714 -0.02223135 -1.6463853
## 46.00000 -0.07477373 -1.7267419
## 46.14286 -0.12644599 -1.8057678
## 46.28571 -0.17728996 -1.8835270
## 46.42857 -0.22734426 -1.9600784
## 
## $福島県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 5.592768 6.941015
## 44.71429 5.655824 7.037451
## 44.85714 5.717392 7.131611
## 45.00000 5.777573 7.223650
## 45.14286 5.836456 7.313704
## 45.28571 5.894123 7.401897
## 45.42857 5.950644 7.488340
## 45.57143 6.006087 7.573132
## 45.71429 6.060510 7.656365
## 45.85714 6.113968 7.738122
## 46.00000 6.166511 7.818479
## 46.14286 6.218183 7.897505
## 46.28571 6.269027 7.975264
## 46.42857 6.319081 8.051815
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 32.01282 31.32280 31.60688 33.06361 33.76381 33.81882 35.64102 34.52517
##  [9] 34.52517 34.52517 34.52517 34.52517 34.52517 34.52517
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 28.10436 26.03535
## 44.71429 26.95859 24.64831
## 44.85714 26.83020 24.30158
## 45.00000 27.90735 25.17779
## 45.14286 28.25407 25.33739
## 45.28571 27.97694 24.88443
## 45.42857 29.48489 26.22604
## 45.57143 27.89288 24.38195
## 45.71429 27.53656 23.83702
## 45.85714 27.19756 23.31855
## 46.00000 26.87355 22.82303
## 46.14286 26.56272 22.34766
## 46.28571 26.26358 21.89016
## 46.42857 25.97490 21.44866
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 35.92128 37.99030
## 44.71429 35.68702 37.99729
## 44.85714 36.38357 38.91219
## 45.00000 38.21986 40.94942
## 45.14286 39.27356 42.19024
## 45.28571 39.66070 42.75321
## 45.42857 41.79715 45.05601
## 45.57143 41.15747 44.66840
## 45.71429 41.51378 45.21333
## 45.85714 41.85279 45.73180
## 46.00000 42.17679 46.22732
## 46.14286 42.48762 46.70269
## 46.28571 42.78677 47.16019
## 46.42857 43.07545 47.60169
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.937845 2.441076 2.699869 2.339599 2.242758 2.198001 2.630520 2.230599
##  [9] 2.301456 2.207061 2.215880 2.192836 2.192842 2.187009
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.9873981 -2.535928
## 44.71429 -0.5197849 -2.087170
## 44.85714 -0.4127462 -2.060465
## 45.00000 -0.7966070 -2.456814
## 45.14286 -0.9263400 -2.603959
## 45.28571 -0.9868593 -2.672823
## 45.42857 -0.5704912 -2.265004
## 45.57143 -1.1114889 -2.880683
## 45.71429 -1.0632875 -2.844475
## 45.85714 -1.1941261 -2.994606
## 46.00000 -1.2045540 -3.015222
## 46.14286 -1.2482462 -3.069845
## 46.28571 -1.2653700 -3.096037
## 46.42857 -1.2882888 -3.128000
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 4.863088 6.411618
## 44.71429 5.401938 6.969323
## 44.85714 5.812485 7.460204
## 45.00000 5.475805 7.136012
## 45.14286 5.411856 7.089475
## 45.28571 5.382862 7.068826
## 45.42857 5.831531 7.526043
## 45.57143 5.572687 7.341881
## 45.71429 5.666199 7.447387
## 45.85714 5.608249 7.408729
## 46.00000 5.636314 7.446982
## 46.14286 5.633918 7.455517
## 46.28571 5.651054 7.481721
## 46.42857 5.662307 7.502019
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 2.011188 3.984698 5.787163 3.114126 4.971138 4.227349 4.880242 4.707218
##  [9] 4.958817 4.943422 5.053160 5.073563 5.128186 5.151011
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -2.4642088 -4.833341
## 44.71429 -1.1679803 -3.895643
## 44.85714  0.4426495 -2.386565
## 45.00000 -2.2842077 -5.141913
## 45.14286 -0.7981827 -3.852276
## 45.28571 -1.6296460 -4.730152
## 45.42857 -1.1257555 -4.305138
## 45.57143 -1.3728524 -4.591447
## 45.71429 -1.2021367 -4.463548
## 45.85714 -1.2737464 -4.564917
## 46.00000 -1.2173459 -4.536751
## 46.14286 -1.2403414 -4.582720
## 46.28571 -1.2256975 -4.589240
## 46.42857 -1.2381019 -4.620294
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143  6.486584  8.855716
## 44.71429  9.137376 11.865040
## 44.85714 11.131677 13.960891
## 45.00000  8.512460 11.370165
## 45.14286 10.740458 13.794551
## 45.28571 10.084344 13.184849
## 45.42857 10.886239 14.065621
## 45.57143 10.787288 14.005882
## 45.71429 11.119770 14.381182
## 45.85714 11.160590 14.451761
## 46.00000 11.323666 14.643072
## 46.14286 11.387467 14.729847
## 46.28571 11.482069 14.845612
## 46.42857 11.540123 14.922315
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1]  81.68965  80.54271  87.39496 103.85638  89.63098  99.38721 103.82419
##  [8]  93.54219  94.60423  98.38389 108.64294  99.94563  94.80621 103.67546
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 67.74676 60.36584
## 44.71429 65.96183 58.24318
## 44.85714 72.20287 64.16066
## 45.00000 88.07672 79.72348
## 45.14286 73.28487 64.63177
## 45.28571 82.49363 73.55071
## 45.42857 86.40033 77.17669
## 45.57143 73.95448 63.58538
## 45.71429 74.20438 63.40535
## 45.85714 77.20300 65.99052
## 46.00000 86.70883 75.09761
## 46.14286 77.28330 65.28659
## 46.28571 71.43834 59.06814
## 46.42857 79.62275 66.89002
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  95.63254 103.0135
## 44.71429  95.12358 102.8422
## 44.85714 102.58706 110.6293
## 45.00000 119.63604 127.9893
## 45.14286 105.97710 114.6302
## 45.28571 116.28079 125.2237
## 45.42857 121.24805 130.4717
## 45.57143 113.12990 123.4990
## 45.71429 115.00408 125.8031
## 45.85714 119.56477 130.7772
## 46.00000 130.57706 142.1883
## 46.14286 122.60797 134.6047
## 46.28571 118.17407 130.5443
## 46.42857 127.72817 140.4609
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 69.97520 62.10239 66.41663 70.33520 74.02483 70.06732 77.83341 72.92394
##  [9] 70.42040 71.70257 74.01957 74.19908 73.15419 75.77265
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 58.30933 52.13380
## 44.71429 49.03525 42.11793
## 44.85714 52.85426 45.67478
## 45.00000 56.29505 48.86265
## 45.14286 59.52264 51.84565
## 45.28571 55.11736 47.20333
## 45.42857 62.44871 54.30454
## 45.57143 56.20992 47.36205
## 45.71429 52.94697 43.69709
## 45.85714 53.65036 44.09410
## 46.00000 55.40658 45.55345
## 46.14286 55.04171 44.90041
## 46.28571 53.46749 43.04597
## 46.42857 55.57047 44.87609
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 81.64107  87.81660
## 44.71429 75.16953  82.08686
## 44.85714 79.97901  87.15849
## 45.00000 84.37534  91.80775
## 45.14286 88.52702  96.20402
## 45.28571 85.01728  92.93131
## 45.42857 93.21812 101.36229
## 45.57143 89.63797  98.48584
## 45.71429 87.89383  97.14371
## 45.85714 89.75478  99.31104
## 46.00000 92.63257 102.48569
## 46.14286 93.35646 103.49776
## 46.28571 92.84090 103.26241
## 46.42857 95.97482 106.66920
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 257.0639 220.8237 322.2095 308.0572 391.6586 363.6560 375.5253 275.5350
##  [9] 235.5469 336.4552 321.8408 404.9951 376.5599 388.0107
## 
## $東京都$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 211.5170 187.4058
## 44.71429 168.1902 140.3278
## 44.85714 266.3772 236.8213
## 45.00000 249.3880 218.3303
## 45.14286 330.4526 298.0520
## 45.28571 300.1668 266.5576
## 45.42857 309.9708 275.2683
## 45.57143 202.8923 164.4375
## 45.71429 158.9598 118.4171
## 45.85714 257.0073 214.9502
## 46.00000 239.8051 196.3780
## 46.14286 320.6086 275.9371
## 46.28571 290.0305 244.2246
## 46.42857 299.5222 252.6792
## 
## $東京都$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 302.6109 326.7220
## 44.71429 273.4571 301.3196
## 44.85714 378.0419 407.5978
## 45.00000 366.7265 397.7842
## 45.14286 452.8647 485.2653
## 45.28571 427.1452 460.7543
## 45.42857 441.0799 475.7824
## 45.57143 348.1778 386.6325
## 45.71429 312.1339 352.6766
## 45.85714 415.9030 457.9601
## 46.00000 403.8765 447.3035
## 46.14286 489.3816 534.0531
## 46.28571 463.0893 508.8952
## 46.42857 476.4992 523.3422
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 111.64417  91.27896 114.75089 129.64154 135.39658 137.27847 136.54846
##  [8] 121.02208 114.51678 122.75346 131.61330 129.02024 130.10773 127.51106
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  91.51566 80.86027
## 44.71429  68.73999 56.80859
## 44.85714  91.37461 78.99995
## 45.00000 105.80818 93.19156
## 45.14286 111.22462 98.42875
## 45.28571 112.81127 99.85911
## 45.42857 111.80398 98.70505
## 45.57143  93.29181 78.61230
## 45.71429  85.62610 70.33230
## 45.85714  93.16994 77.50937
## 46.00000 101.48979 85.54337
## 46.14286  98.41530 82.21403
## 46.28571  99.04726 82.60483
## 46.42857  96.00870 79.33236
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 131.7727 142.4281
## 44.71429 113.8179 125.7493
## 44.85714 138.1272 150.5018
## 45.00000 153.4749 166.0915
## 45.14286 159.5685 172.3644
## 45.28571 161.7457 174.6978
## 45.42857 161.2929 174.3919
## 45.57143 148.7523 163.4319
## 45.71429 143.4075 158.7013
## 45.85714 152.3370 167.9976
## 46.00000 161.7368 177.6832
## 46.14286 159.6252 175.8265
## 46.28571 161.1682 177.6106
## 46.42857 159.0134 175.6898
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 2.7138714 1.9688610 1.6657951 3.3945114 1.9165523 1.3081232 0.9523202
##  [8] 1.1633087 1.0870914 1.0252759 0.9751409 0.9344792 0.9015008 0.8747539
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%         95%
## 44.57143  0.92090313 -0.02823693
## 44.71429  0.11486398 -0.86658275
## 44.85714 -0.22727365 -1.22940370
## 45.00000  1.47617554  0.46066985
## 45.14286 -0.01822426 -1.04243308
## 45.28571 -0.63739205 -1.66728561
## 45.42857 -1.00022671 -2.03384260
## 45.57143 -0.82460349 -1.87694064
## 45.71429 -0.90823040 -1.96448996
## 45.85714 -0.97490492 -2.03373669
## 46.00000 -1.02822975 -2.08875010
## 46.14286 -1.07098688 -2.13261649
## 46.28571 -1.10534242 -2.16770107
## 46.42857 -1.13299468 -2.19583261
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 4.506840 5.455980
## 44.71429 3.822858 4.804305
## 44.85714 3.558864 4.560994
## 45.00000 5.312847 6.328353
## 45.14286 3.851329 4.875538
## 45.28571 3.253638 4.283532
## 45.42857 2.904867 3.938483
## 45.57143 3.151221 4.203558
## 45.71429 3.082413 4.138673
## 45.85714 3.025457 4.084289
## 46.00000 2.978511 4.039032
## 46.14286 2.939945 4.001575
## 46.28571 2.908344 3.970703
## 46.42857 2.882503 3.945340
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.8358558 0.9535486 0.9271928 1.1579311 1.0419649 1.0788004 1.0836073
##  [8] 1.1208017 1.1187183 1.1342749 1.1439928 1.1572870 1.1655803 1.1754692
## 
## $富山県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.622582 -2.924000
## 44.71429 -1.637268 -3.008763
## 44.85714 -1.813021 -3.263603
## 45.00000 -1.692191 -3.200955
## 45.14286 -1.978683 -3.577717
## 45.28571 -2.039085 -3.689594
## 45.42857 -2.123663 -3.821489
## 45.57143 -2.160615 -3.897693
## 45.71429 -2.232442 -4.006439
## 45.85714 -2.273666 -4.077721
## 46.00000 -2.313918 -4.144426
## 46.14286 -2.343727 -4.197052
## 46.28571 -2.373439 -4.246883
## 46.42857 -2.396398 -4.287230
## 
## $富山県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 3.294293 4.595712
## 44.71429 3.544365 4.915860
## 44.85714 3.667407 5.117989
## 45.00000 4.008053 5.516817
## 45.14286 4.062613 5.661647
## 45.28571 4.196686 5.847195
## 45.42857 4.290877 5.988703
## 45.57143 4.402219 6.139296
## 45.71429 4.469878 6.243876
## 45.85714 4.542216 6.346270
## 46.00000 4.601904 6.432411
## 46.14286 4.658301 6.511626
## 46.28571 4.704600 6.578043
## 46.42857 4.747336 6.638168
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.023451 1.023451 1.023451 1.023451 1.023451 1.023451 1.023451 1.023451
##  [9] 1.023451 1.023451 1.023451 1.023451 1.023451 1.023451
## 
## $石川県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -2.658667 -4.607862
## 44.71429 -2.865635 -4.924392
## 44.85714 -3.062131 -5.224908
## 45.00000 -3.249602 -5.511619
## 45.14286 -3.429186 -5.786270
## 45.28571 -3.601803 -6.050264
## 45.42857 -3.768205 -6.304755
## 45.57143 -3.929019 -6.550699
## 45.71429 -4.084774 -6.788904
## 45.85714 -4.235917 -7.020059
## 46.00000 -4.382837 -7.244753
## 46.14286 -4.525868 -7.463501
## 46.28571 -4.665305 -7.676750
## 46.42857 -4.801404 -7.884896
## 
## $石川県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 4.705569 6.654765
## 44.71429 4.912537 6.971295
## 44.85714 5.109034 7.271811
## 45.00000 5.296504 7.558522
## 45.14286 5.476088 7.833172
## 45.28571 5.648705 8.097167
## 45.42857 5.815107 8.351657
## 45.57143 5.975922 8.597601
## 45.71429 6.131676 8.835807
## 45.85714 6.282820 9.066961
## 46.00000 6.429740 9.291656
## 46.14286 6.572771 9.510403
## 46.28571 6.712207 9.723653
## 46.42857 6.848307 9.931799
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.3078435 1.4759785 0.9888525 1.2507580 1.1887399 0.9999160 1.1458638
##  [8] 1.0484902 0.9946385 1.0543695 0.9817240 0.9758957 0.9902307 0.9480790
## 
## $福井県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.5220364 -1.490716
## 44.71429 -0.4633647 -1.489991
## 44.85714 -1.1517531 -2.284921
## 45.00000 -1.1327876 -2.394560
## 45.14286 -1.2389113 -2.524032
## 45.28571 -1.5275693 -2.865539
## 45.42857 -1.4527736 -2.828409
## 45.57143 -1.5749917 -2.963779
## 45.71429 -1.6746415 -3.087673
## 45.85714 -1.6394007 -3.065396
## 46.00000 -1.7264640 -3.160092
## 46.14286 -1.7518849 -3.195885
## 46.28571 -1.7472349 -3.196362
## 46.42857 -1.7972626 -3.250559
## 
## $福井県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 3.137723 4.106403
## 44.71429 3.415322 4.441948
## 44.85714 3.129458 4.262626
## 45.00000 3.634304 4.896076
## 45.14286 3.616391 4.901512
## 45.28571 3.527401 4.865371
## 45.42857 3.744501 5.120137
## 45.57143 3.671972 5.060760
## 45.71429 3.663918 5.076950
## 45.85714 3.748140 5.174136
## 46.00000 3.689912 5.123540
## 46.14286 3.703676 5.147676
## 46.28571 3.727696 5.176823
## 46.42857 3.693421 5.146717
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 3.197676 4.202169 4.517712 4.616835 4.647972 4.657753 4.660826 4.661791
##  [9] 4.662094 4.662190 4.662220 4.662229 4.662232 4.662233
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 1.411854 0.4664966
## 44.71429 2.225437 1.1790178
## 44.85714 2.465143 1.3785787
## 45.00000 2.513309 1.3997704
## 45.14286 2.500724 1.3640400
## 45.28571 2.469436 1.3110106
## 45.42857 2.432737 1.2532582
## 45.57143 2.394795 1.1947198
## 45.71429 2.356899 1.1366021
## 45.85714 2.319433 1.0792532
## 46.00000 2.282500 1.0227531
## 46.14286 2.246114 0.9670994
## 46.28571 2.210261 0.9122662
## 46.42857 2.174924 0.8582216
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 4.983498 5.928855
## 44.71429 6.178901 7.225319
## 44.85714 6.570281 7.656846
## 45.00000 6.720360 7.833899
## 45.14286 6.795220 7.931904
## 45.28571 6.846071 8.004496
## 45.42857 6.888915 8.068394
## 45.57143 6.928787 8.128862
## 45.71429 6.967290 8.187587
## 45.85714 7.004946 8.245126
## 46.00000 7.041939 8.301686
## 46.14286 7.078344 8.357358
## 46.28571 7.114203 8.412198
## 46.42857 7.149542 8.466244
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 16.10522 16.10522 16.10522 16.10522 16.10522 16.10522 16.10522 16.10522
##  [9] 16.10522 16.10522 16.10522 16.10522 16.10522 16.10522
## 
## $長野県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 13.09689 11.504369
## 44.71429 12.88171 11.175283
## 44.85714 12.68002 10.866831
## 45.00000 12.48957 10.575558
## 45.14286 12.30866 10.298879
## 45.28571 12.13598 10.034796
## 45.42857 11.97052  9.781733
## 45.57143 11.81142  9.538415
## 45.71429 11.65801  9.303796
## 45.85714 11.50972  9.077005
## 46.00000 11.36607  8.857306
## 46.14286 11.22664  8.644074
## 46.28571 11.09109  8.436769
## 46.42857 10.95911  8.234923
## 
## $長野県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 19.11356 20.70608
## 44.71429 19.32874 21.03517
## 44.85714 19.53043 21.34362
## 45.00000 19.72088 21.63489
## 45.14286 19.90179 21.91157
## 45.28571 20.07446 22.17565
## 45.42857 20.23993 22.42872
## 45.57143 20.39903 22.67203
## 45.71429 20.55244 22.90665
## 45.85714 20.70073 23.13344
## 46.00000 20.84438 23.35314
## 46.14286 20.98381 23.56637
## 46.28571 21.11936 23.77368
## 46.42857 21.25134 23.97553
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 5.543688 5.971315 7.700523 6.440902 9.760764 7.524897 4.069926 5.056375
##  [9] 4.855261 5.955780 5.542439 6.849051 4.871947 4.219940
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                   80%         95%
## 44.57143  2.164715125  0.37599492
## 44.71429  2.125570405  0.08975629
## 44.85714  3.442270121  1.18808713
## 45.00000  1.941648193 -0.44011289
## 45.14286  5.090869654  2.61877680
## 45.28571  2.737775186  0.20362574
## 45.42857 -0.800861077 -3.37929993
## 45.57143 -0.137479263 -2.88693961
## 45.71429 -0.481114785 -3.30602125
## 45.85714  0.500058225 -2.38802626
## 46.00000  0.005016403 -2.92631812
## 46.14286  1.250312712 -1.71348014
## 46.28571 -0.772468563 -3.76044144
## 46.42857 -1.459413153 -4.46588106
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  8.922661 10.71138
## 44.71429  9.817059 11.85287
## 44.85714 11.958776 14.21296
## 45.00000 10.940156 13.32192
## 45.14286 14.430659 16.90275
## 45.28571 12.312020 14.84617
## 45.42857  8.940713 11.51915
## 45.57143 10.250229 12.99969
## 45.71429 10.191636 13.01654
## 45.85714 11.411501 14.29959
## 46.00000 11.079862 14.01120
## 46.14286 12.447789 15.41158
## 46.28571 10.516362 13.50433
## 46.42857  9.899293 12.90576
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 25.57857 17.35091 23.34524 27.26140 21.35253 21.07513 29.45920 25.45972
##  [9] 19.89015 24.74606 27.63457 22.08634 22.58298 28.66759
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 21.55341 19.422618
## 44.71429 12.69646 10.232542
## 44.85714 18.47026 15.889601
## 45.00000 22.19037 19.505923
## 45.14286 15.81970 12.890803
## 45.28571 15.23714 12.146700
## 45.42857 23.47693 20.310115
## 45.57143 19.10824 15.745967
## 45.71429 13.15412  9.588279
## 45.85714 17.83024 14.169231
## 46.00000 20.51818 16.750993
## 46.14286 14.62980 10.682541
## 46.28571 14.89787 10.829614
## 46.42857 20.82779 16.677643
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 29.60373 31.73452
## 44.71429 22.00536 24.46928
## 44.85714 28.22023 30.80089
## 45.00000 32.33243 35.01688
## 45.14286 26.88536 29.81426
## 45.28571 26.91311 30.00355
## 45.42857 35.44146 38.60828
## 45.57143 31.81121 35.17348
## 45.71429 26.62618 30.19202
## 45.85714 31.66188 35.32290
## 46.00000 34.75096 38.51815
## 46.14286 29.54288 33.49013
## 46.28571 30.26809 34.33634
## 46.42857 36.50740 40.65755
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 139.4465 124.5385 142.0689 140.4350 160.6640 165.5160 165.3748 157.8908
##  [9] 148.4996 144.1792 147.8950 156.5164 163.4230 163.5752
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 125.0464 117.42341
## 44.71429 107.3199  98.20488
## 44.85714 122.0954 111.52200
## 45.00000 118.1953 106.42228
## 45.14286 136.7771 124.13206
## 45.28571 140.4024 127.10802
## 45.42857 139.1367 125.24713
## 45.57143 128.2817 112.60766
## 45.71429 116.2031  99.10632
## 45.85714 109.0935  90.52029
## 46.00000 110.3842  90.52713
## 46.14286 117.1444  96.30213
## 46.28571 122.5970 100.98497
## 46.42857 121.3870  99.05397
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 153.8466 161.4696
## 44.71429 141.7572 150.8722
## 44.85714 162.0424 172.6158
## 45.00000 162.6747 174.4477
## 45.14286 184.5510 197.1960
## 45.28571 190.6296 203.9239
## 45.42857 191.6128 205.5024
## 45.57143 187.4998 203.1739
## 45.71429 180.7961 197.8929
## 45.85714 179.2649 197.8382
## 46.00000 185.4059 205.2629
## 46.14286 195.8884 216.7307
## 46.28571 204.2490 225.8609
## 46.42857 205.7633 228.0964
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 4.312625 4.312625 4.312625 4.312625 4.312625 4.312625 4.312625 4.312625
##  [9] 4.312625 4.312625 4.312625 4.312625 4.312625 4.312625
## 
## $三重県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%       95%
## 44.57143  0.61552624 -1.341600
## 44.71429  0.41080262 -1.654697
## 44.85714  0.21629781 -1.952167
## 45.00000  0.03061904 -2.236138
## 45.14286 -0.14733617 -2.508297
## 45.28571 -0.31845827 -2.770006
## 45.42857 -0.48347872 -3.022383
## 45.57143 -0.64300710 -3.266360
## 45.71429 -0.79755779 -3.502725
## 45.85714 -0.94756955 -3.732148
## 46.00000 -1.09342026 -3.955208
## 46.14286 -1.23543808 -4.172405
## 46.28571 -1.37391020 -4.384180
## 46.42857 -1.50908963 -4.590919
## 
## $三重県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  8.009724  9.96685
## 44.71429  8.214448 10.27995
## 44.85714  8.408953 10.57742
## 45.00000  8.594631 10.86139
## 45.14286  8.772587 11.13355
## 45.28571  8.943709 11.39526
## 45.42857  9.108729 11.64763
## 45.57143  9.268257 11.89161
## 45.71429  9.422808 12.12798
## 45.85714  9.572820 12.35740
## 46.00000  9.718671 12.58046
## 46.14286  9.860688 12.79766
## 46.28571  9.999161 13.00943
## 46.42857 10.134340 13.21617
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 4.525012 7.405969 5.994838 6.917744 6.473232 6.769128 6.629273 6.724227
##  [9] 6.680286 6.710788 6.697004 6.706812 6.702496 6.705654
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%        95%
## 44.57143 0.6161966 -1.4530052
## 44.71429 3.4381048  1.3376446
## 44.85714 1.5992036 -0.7277044
## 45.00000 2.4414971  0.0719149
## 45.14286 1.8341292 -0.6216634
## 45.28571 2.0475143 -0.4519570
## 45.42857 1.8043441 -0.7498189
## 45.57143 1.8192275 -0.7773226
## 45.71429 1.6902131 -0.9513719
## 45.85714 1.6434574 -1.0390254
## 46.00000 1.5517581 -1.1719704
## 46.14286 1.4868118 -1.2764896
## 46.28571 1.4082691 -1.3943254
## 46.42857 1.3388602 -1.5021489
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  8.433827 10.50303
## 44.71429 11.373832 13.47429
## 44.85714 10.390472 12.71738
## 45.00000 11.393992 13.76357
## 45.14286 11.112334 13.56813
## 45.28571 11.490741 13.99021
## 45.42857 11.454201 14.00836
## 45.57143 11.629227 14.22578
## 45.71429 11.670359 14.31194
## 45.85714 11.778119 14.46060
## 46.00000 11.842249 14.56598
## 46.14286 11.926813 14.69011
## 46.28571 11.996723 14.79932
## 46.42857 12.072448 14.91346
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 21.26919 17.24982 18.70452 18.52539 18.71746 22.05834 20.66255 20.48723
##  [9] 18.59697 19.13896 19.88159 19.74369 20.93439 20.33136
## 
## $京都府$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 14.92552 11.567379
## 44.71429 10.30948  6.635481
## 44.85714 11.53346  7.737330
## 45.00000 11.19610  7.316213
## 45.14286 11.24965  7.296428
## 45.28571 14.45898 10.436114
## 45.42857 12.93510  8.844438
## 45.57143 12.36247  8.061481
## 45.71429 10.25495  5.838954
## 45.85714 10.62262  6.114342
## 46.00000 11.20430  6.610814
## 46.14286 10.91110  6.235415
## 46.28571 11.94996  7.193892
## 46.42857 11.19783  6.362823
## 
## $京都府$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 27.61286 30.97099
## 44.71429 24.19017 27.86416
## 44.85714 25.87559 29.67172
## 45.00000 25.85468 29.73457
## 45.14286 26.18527 30.13848
## 45.28571 29.65771 33.68057
## 45.42857 28.38999 32.48066
## 45.57143 28.61200 32.91299
## 45.71429 26.93899 31.35499
## 45.85714 27.65530 32.16358
## 46.00000 28.55888 33.15236
## 46.14286 28.57627 33.25196
## 46.28571 29.91882 34.67490
## 46.42857 29.46490 34.29990
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 247.5877 205.7018 295.6124 280.0010 285.6252 313.0152 329.8621 297.2699
##  [9] 257.5138 344.7178 344.8946 340.9183 364.4687 380.0143
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 223.2154 210.3134
## 44.71429 178.6072 164.2641
## 44.85714 266.0451 250.3931
## 45.00000 248.1524 231.2928
## 45.14286 251.6482 233.6618
## 45.28571 277.0354 257.9888
## 45.42857 291.9853 271.9345
## 45.57143 253.8885 230.9239
## 45.71429 211.1504 186.6070
## 45.85714 295.5528 269.5264
## 46.00000 293.0793 265.6499
## 46.14286 286.5818 257.8178
## 46.28571 307.7230 277.6836
## 46.42857 320.9574 289.6946
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 271.9601 284.8621
## 44.71429 232.7965 247.1395
## 44.85714 325.1798 340.8318
## 45.00000 311.8496 328.7092
## 45.14286 319.6023 337.5886
## 45.28571 348.9950 368.0416
## 45.42857 367.7389 387.7897
## 45.57143 340.6512 363.6159
## 45.71429 303.8772 328.4205
## 45.85714 393.8828 419.9092
## 46.00000 396.7099 424.1392
## 46.14286 395.2548 424.0188
## 46.28571 421.2145 451.2539
## 46.42857 439.0711 470.3339
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 68.19083 62.50715 81.45273 86.18912 91.39916 85.71548 90.45188 85.33223
##  [9] 82.64021 91.61359 93.85693 96.32461 93.63259 95.87594
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 58.86222 53.92396
## 44.71429 52.51189 47.22072
## 44.85714 70.83257 65.21060
## 45.00000 74.97885 69.04449
## 45.14286 79.62832 73.39722
## 45.28571 73.40959 66.89524
## 45.42857 77.63324 70.84746
## 45.57143 70.21802 62.21704
## 45.71429 66.62726 58.15051
## 45.85714 74.74970 65.82250
## 46.00000 76.18304 66.82706
## 46.14286 77.87625 68.11028
## 46.28571 74.44099 64.28157
## 46.42857 75.96882 65.43063
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143  77.51943  82.45769
## 44.71429  72.50242  77.79359
## 44.85714  92.07288  97.69486
## 45.00000  97.39939 103.33375
## 45.14286 103.16999 109.40110
## 45.28571  98.02138 104.53572
## 45.42857 103.27051 110.05629
## 45.57143 100.44643 108.44741
## 45.71429  98.65317 107.12992
## 45.85714 108.47747 117.40467
## 46.00000 111.53081 120.88680
## 46.14286 114.77296 124.53893
## 46.28571 112.82420 122.98361
## 46.42857 115.78305 126.32124
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 20.50334 20.38088 19.24309 20.92121 20.70416 20.93381 20.67076 20.42050
##  [9] 20.61543 20.53670 20.69494 20.61907 20.61408 20.60692
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 16.74820 14.760346
## 44.71429 16.39113 14.279077
## 44.85714 14.61708 12.168214
## 45.00000 16.17661 13.664965
## 45.14286 15.76158 13.145131
## 45.28571 15.69661 12.924201
## 45.42857 15.21116 12.321023
## 45.57143 14.65126 11.597202
## 45.71429 14.65291 11.496544
## 45.85714 14.35072 11.076066
## 46.00000 14.31205 10.933148
## 46.14286 14.03605 10.551208
## 46.28571 13.82632 10.233100
## 46.42857 13.63235  9.940234
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 24.25849 26.24634
## 44.71429 24.37064 26.48269
## 44.85714 23.86910 26.31796
## 45.00000 25.66581 28.17745
## 45.14286 25.64674 28.26318
## 45.28571 26.17102 28.94343
## 45.42857 26.13035 29.02049
## 45.57143 26.18975 29.24380
## 45.71429 26.57795 29.73432
## 45.85714 26.72268 29.99734
## 46.00000 27.07783 30.45673
## 46.14286 27.20209 30.68694
## 46.28571 27.40184 30.99506
## 46.42857 27.58150 31.27361
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 6.439892 6.264398 6.404398 5.441678 5.748608 5.925012 5.057572 5.548776
##  [9] 5.690051 5.568451 5.673116 5.583027 5.660570 5.593827
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 4.500948 3.474533
## 44.71429 4.153840 3.036578
## 44.85714 4.226534 3.073642
## 45.00000 3.124151 1.897326
## 45.14286 3.361349 2.097610
## 45.28571 3.419155 2.092635
## 45.42857 2.481094 1.117189
## 45.57143 2.932169 1.547021
## 45.71429 3.026646 1.616724
## 45.85714 2.828287 1.377731
## 46.00000 2.884503 1.408301
## 46.14286 2.724924 1.211935
## 46.28571 2.753245 1.214200
## 46.42857 2.622504 1.049581
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 8.378836  9.405251
## 44.71429 8.374956  9.492218
## 44.85714 8.582263  9.735155
## 45.00000 7.759204  8.986029
## 45.14286 8.135867  9.399606
## 45.28571 8.430868  9.757388
## 45.42857 7.634050  8.997955
## 45.57143 8.165383  9.550531
## 45.71429 8.353456  9.763378
## 45.85714 8.308615  9.759171
## 46.00000 8.461729  9.937931
## 46.14286 8.441131  9.954120
## 46.28571 8.567895 10.106940
## 46.42857 8.565149 10.138072
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.4454700 0.4247067 0.4085324 0.3959329 0.3861182 0.3784726 0.3725169
##  [8] 0.3678775 0.3642634 0.3614482 0.3592551 0.3575468 0.3562160 0.3551794
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%        95%
## 44.57143 -0.4988210 -0.9986984
## 44.71429 -0.5345714 -1.0423825
## 44.85714 -0.5604112 -1.0733389
## 45.00000 -0.5793809 -1.0956808
## 45.14286 -0.5934911 -1.1120649
## 45.28571 -0.6041057 -1.1242512
## 45.42857 -0.6121697 -1.1334313
## 45.57143 -0.6183501 -1.1404275
## 45.71429 -0.6231254 -1.1458175
## 45.85714 -0.6268436 -1.1500136
## 46.00000 -0.6297609 -1.1533144
## 46.14286 -0.6320681 -1.1559386
## 46.28571 -0.6339083 -1.1580485
## 46.42857 -0.6353897 -1.1597653
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.389761 1.889638
## 44.71429 1.383985 1.891796
## 44.85714 1.377476 1.890404
## 45.00000 1.371247 1.887547
## 45.14286 1.365727 1.884301
## 45.28571 1.361051 1.881196
## 45.42857 1.357203 1.878465
## 45.57143 1.354105 1.876182
## 45.71429 1.351652 1.874344
## 45.85714 1.349740 1.872910
## 46.00000 1.348271 1.871825
## 46.14286 1.347162 1.871032
## 46.28571 1.346340 1.870481
## 46.42857 1.345749 1.870124
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951
##  [8] 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951 0.4622951
## 
## $島根県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -6.319762 -9.909966
## 44.71429 -6.319762 -9.909966
## 44.85714 -6.319762 -9.909966
## 45.00000 -6.319762 -9.909966
## 45.14286 -6.319762 -9.909966
## 45.28571 -6.319762 -9.909966
## 45.42857 -6.319762 -9.909966
## 45.57143 -6.319762 -9.909966
## 45.71429 -6.319762 -9.909966
## 45.85714 -6.319762 -9.909966
## 46.00000 -6.319762 -9.909966
## 46.14286 -6.319762 -9.909966
## 46.28571 -6.319762 -9.909966
## 46.42857 -6.319762 -9.909966
## 
## $島根県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 7.244352 10.83456
## 44.71429 7.244352 10.83456
## 44.85714 7.244352 10.83456
## 45.00000 7.244352 10.83456
## 45.14286 7.244352 10.83456
## 45.28571 7.244352 10.83456
## 45.42857 7.244352 10.83456
## 45.57143 7.244352 10.83456
## 45.71429 7.244352 10.83456
## 45.85714 7.244352 10.83456
## 46.00000 7.244352 10.83456
## 46.14286 7.244352 10.83456
## 46.28571 7.244352 10.83456
## 46.42857 7.244352 10.83456
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 10.164759  9.367197  7.606750  8.820934  9.152617  9.130379  8.956932
##  [8]  8.912891  9.219149  9.348761  8.552549  8.433889  7.943424  8.044417
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 7.536362 6.144973
## 44.71429 6.417570 4.856132
## 44.85714 4.535289 2.909355
## 45.00000 5.675898 4.011016
## 45.14286 5.949171 4.253369
## 45.28571 5.874178 4.150449
## 45.42857 5.650463 3.900124
## 45.57143 5.500386 3.693915
## 45.71429 5.733835 3.888821
## 45.85714 5.801865 3.924251
## 46.00000 4.948526 3.040670
## 46.14286 4.774856 2.837881
## 46.28571 4.230646 2.265221
## 46.42857 4.278827 2.285444
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 12.79316 14.18454
## 44.71429 12.31682 13.87826
## 44.85714 10.67821 12.30415
## 45.00000 11.96597 13.63085
## 45.14286 12.35606 14.05186
## 45.28571 12.38658 14.11031
## 45.42857 12.26340 14.01374
## 45.57143 12.32540 14.13187
## 45.71429 12.70446 14.54948
## 45.85714 12.89566 14.77327
## 46.00000 12.15657 14.06443
## 46.14286 12.09292 14.02990
## 46.28571 11.65620 13.62163
## 46.42857 11.81001 13.80339
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 4.075919 3.923187 4.977527 4.832622 4.303874 4.494150 4.734106 4.581643
##  [9] 4.486918 4.588173 4.616764 4.556873 4.554531 4.586832
## 
## $広島県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143  0.5549498 -1.308938
## 44.71429  0.0140615 -2.055304
## 44.85714  0.8765464 -1.294382
## 45.00000  0.1278863 -2.362650
## 45.14286 -0.9421026 -3.719155
## 45.28571 -1.0364839 -3.964225
## 45.42857 -1.1026878 -4.192500
## 45.57143 -1.6362388 -4.927787
## 45.71429 -2.0518501 -5.513265
## 45.85714 -2.2214584 -5.826260
## 46.00000 -2.4780609 -6.233834
## 46.14286 -2.8245079 -6.731975
## 46.28571 -3.0876115 -7.133117
## 46.42857 -3.3045167 -7.481944
## 
## $広島県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143  7.596887  9.460775
## 44.71429  7.832312  9.901678
## 44.85714  9.078508 11.249436
## 45.00000  9.537357 12.027894
## 45.14286  9.549851 12.326904
## 45.28571 10.024785 12.952526
## 45.42857 10.570900 13.660712
## 45.57143 10.799525 14.091073
## 45.71429 11.025686 14.487101
## 45.85714 11.397805 15.002607
## 46.00000 11.711589 15.467362
## 46.14286 11.938255 15.845722
## 46.28571 12.196673 16.242179
## 46.42857 12.478181 16.655609
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 7.168147 7.168147 7.168147 7.168147 7.168147 7.168147 7.168147 7.168147
##  [9] 7.168147 7.168147 7.168147 7.168147 7.168147 7.168147
## 
## $山口県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 5.033261 3.903121
## 44.71429 4.925039 3.737610
## 44.85714 4.821804 3.579725
## 45.00000 4.722923 3.428499
## 45.14286 4.627888 3.283156
## 45.28571 4.536282 3.143057
## 45.42857 4.447760 3.007674
## 45.57143 4.362028 2.876559
## 45.71429 4.278839 2.749333
## 45.85714 4.197980 2.625668
## 46.00000 4.119264 2.505283
## 46.14286 4.042530 2.387928
## 46.28571 3.967635 2.273386
## 46.42857 3.894453 2.161464
## 
## $山口県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%      95%
## 44.57143  9.303032 10.43317
## 44.71429  9.411254 10.59868
## 44.85714  9.514490 10.75657
## 45.00000  9.613371 10.90779
## 45.14286  9.708406 11.05314
## 45.28571  9.800011 11.19324
## 45.42857  9.888534 11.32862
## 45.57143  9.974265 11.45973
## 45.71429 10.057454 11.58696
## 45.85714 10.138314 11.71062
## 46.00000 10.217029 11.83101
## 46.14286 10.293764 11.94837
## 46.28571 10.368659 12.06291
## 46.42857 10.441841 12.17483
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1]  0.6933696  0.6661911  0.7840810  0.4732044  0.4690593 -0.0448563
##  [7]  0.8613003  0.6496832  0.4725550  0.3457803  0.6169525  0.6182366
## [13]  0.6949097  0.4111218
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.037404 -1.953620
## 44.71429 -1.148042 -2.108439
## 44.85714 -1.048924 -2.019259
## 45.00000 -1.378383 -2.358554
## 45.14286 -1.400925 -2.390835
## 45.28571 -1.933059 -2.932613
## 45.42857 -1.044947 -2.054053
## 45.57143 -1.349739 -2.408169
## 45.71429 -1.560635 -2.636940
## 45.85714 -1.711407 -2.800416
## 46.00000 -1.463956 -2.565522
## 46.14286 -1.486125 -2.600107
## 46.28571 -1.432647 -2.558908
## 46.42857 -1.739380 -2.877787
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 2.424143 3.340359
## 44.71429 2.480424 3.440821
## 44.85714 2.617086 3.587421
## 45.00000 2.324792 3.304963
## 45.14286 2.339044 3.328954
## 45.28571 1.843347 2.842901
## 45.42857 2.767547 3.776653
## 45.57143 2.649105 3.707535
## 45.71429 2.505745 3.582050
## 45.85714 2.402968 3.491977
## 46.00000 2.697861 3.799427
## 46.14286 2.722599 3.836580
## 46.28571 2.822467 3.948727
## 46.42857 2.561624 3.700031
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.5070130 0.6588375 0.6588375 0.6588375 0.6588375 0.6588375 0.6588375
##  [8] 0.6588375 0.6588375 0.6588375 0.6588375 0.6588375 0.6588375 0.6588375
## 
## $香川県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.8598693 -1.583453
## 44.71429 -0.7717718 -1.529091
## 44.85714 -0.7758890 -1.535387
## 45.00000 -0.7799945 -1.541666
## 45.14286 -0.7840883 -1.547927
## 45.28571 -0.7881705 -1.554170
## 45.42857 -0.7922412 -1.560396
## 45.57143 -0.7963005 -1.566604
## 45.71429 -0.8003486 -1.572795
## 45.85714 -0.8043854 -1.578969
## 46.00000 -0.8084111 -1.585126
## 46.14286 -0.8124259 -1.591266
## 46.28571 -0.8164297 -1.597389
## 46.42857 -0.8204226 -1.603496
## 
## $香川県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.873895 2.597479
## 44.71429 2.089447 2.846766
## 44.85714 2.093564 2.853062
## 45.00000 2.097669 2.859341
## 45.14286 2.101763 2.865602
## 45.28571 2.105845 2.871845
## 45.42857 2.109916 2.878071
## 45.57143 2.113976 2.884279
## 45.71429 2.118024 2.890470
## 45.85714 2.122060 2.896644
## 46.00000 2.126086 2.902801
## 46.14286 2.130101 2.908941
## 46.28571 2.134105 2.915064
## 46.42857 2.138098 2.921171
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.04664365 0.42839468 0.22069439 0.46444548 0.70697896 0.66867691
##  [7] 0.27186680 0.44862420 0.33263953 0.45705974 0.34723572 0.45125456
## [13] 0.35530945 0.44468074
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.599332 -2.470659
## 44.71429 -1.264811 -2.161140
## 44.85714 -1.498352 -2.408361
## 45.00000 -1.255208 -2.165537
## 45.14286 -1.016997 -1.929615
## 45.28571 -1.057435 -1.971183
## 45.42857 -1.456551 -2.371519
## 45.57143 -1.283048 -2.199740
## 45.71429 -1.403181 -2.322069
## 45.85714 -1.279131 -2.198215
## 46.00000 -1.389973 -2.309595
## 46.14286 -1.286568 -2.206515
## 46.28571 -1.383122 -2.303393
## 46.42857 -1.294251 -2.214786
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.692619 2.563946
## 44.71429 2.121601 3.017930
## 44.85714 1.939741 2.849749
## 45.00000 2.184099 3.094428
## 45.14286 2.430955 3.343573
## 45.28571 2.394788 3.308537
## 45.42857 2.000284 2.915253
## 45.57143 2.180297 3.096989
## 45.71429 2.068460 2.987348
## 45.85714 2.193251 3.112335
## 46.00000 2.084444 3.004066
## 46.14286 2.189077 3.109024
## 46.28571 2.093741 3.014012
## 46.42857 2.183612 3.104147
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.1478021 0.1704869 0.1914475 0.2108150 0.2287103 0.2452454 0.2605237
##  [8] 0.2746407 0.2876848 0.2997373 0.3108737 0.3211637 0.3306716 0.3394568
## 
## $高知県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.549758 -2.448393
## 44.71429 -1.548805 -2.458943
## 44.85714 -1.546183 -2.466029
## 45.00000 -1.542320 -2.470374
## 45.14286 -1.537555 -2.472559
## 45.28571 -1.532153 -2.473050
## 45.42857 -1.526324 -2.472224
## 45.57143 -1.520236 -2.470386
## 45.71429 -1.514018 -2.467782
## 45.85714 -1.507773 -2.464611
## 46.00000 -1.501580 -2.461035
## 46.14286 -1.495500 -2.457183
## 46.28571 -1.489578 -2.453161
## 46.42857 -1.483850 -2.449050
## 
## $高知県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.845363 2.743997
## 44.71429 1.889779 2.799917
## 44.85714 1.929078 2.848924
## 45.00000 1.963950 2.892004
## 45.14286 1.994975 2.929980
## 45.28571 2.022643 2.963541
## 45.42857 2.047372 2.993272
## 45.57143 2.069517 3.019667
## 45.71429 2.089387 3.043151
## 45.85714 2.107247 3.064085
## 46.00000 2.123327 3.082782
## 46.14286 2.137827 3.099511
## 46.28571 2.150922 3.114504
## 46.42857 2.162763 3.127963
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1]  8.738409 11.387736 17.060371 17.768415 15.214100 13.678770  9.909482
##  [8] 10.320609 12.429467 16.693386 17.067908 15.609065 13.409703 11.247183
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                  80%        95%
## 44.57143  -4.8068231 -11.977236
## 44.71429  -4.5289529 -12.954739
## 44.85714  -0.1380849  -9.242397
## 45.00000  -0.1264307  -9.599390
## 45.14286  -3.2862994 -13.079819
## 45.28571  -5.7082946 -15.971187
## 45.42857 -10.8221554 -21.796821
## 45.57143 -13.0119367 -25.363440
## 45.71429 -12.7402483 -26.064291
## 45.85714  -9.6969108 -23.667090
## 46.00000 -10.1158384 -24.506044
## 46.14286 -12.2641217 -27.019295
## 46.28571 -15.3263416 -30.538285
## 46.42857 -18.6652169 -34.499885
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 22.28364 29.45405
## 44.71429 27.30443 35.73021
## 44.85714 34.25883 43.36314
## 45.00000 35.66326 45.13622
## 45.14286 33.71450 43.50802
## 45.28571 33.06584 43.32873
## 45.42857 30.64112 41.61579
## 45.57143 33.65315 46.00466
## 45.71429 37.59918 50.92322
## 45.85714 43.08368 57.05386
## 46.00000 44.25165 58.64186
## 46.14286 43.48225 58.23742
## 46.28571 42.14575 57.35769
## 46.42857 41.15958 56.99425
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.277253 1.277253 1.277253 1.277253 1.277253 1.277253 1.277253 1.277253
##  [9] 1.277253 1.277253 1.277253 1.277253 1.277253 1.277253
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.7193907 -1.776350
## 44.71429 -0.7943749 -1.891029
## 44.85714 -0.8667382 -2.001699
## 45.00000 -0.9367376 -2.108754
## 45.14286 -1.0045907 -2.212526
## 45.28571 -1.0704835 -2.313300
## 45.42857 -1.1345768 -2.411323
## 45.57143 -1.1970104 -2.506806
## 45.71429 -1.2579068 -2.599940
## 45.85714 -1.3173744 -2.690887
## 46.00000 -1.3755093 -2.779797
## 46.14286 -1.4323972 -2.866799
## 46.28571 -1.4881150 -2.952013
## 46.42857 -1.5427322 -3.035542
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 3.273897 4.330857
## 44.71429 3.348881 4.445535
## 44.85714 3.421245 4.556205
## 45.00000 3.491244 4.663260
## 45.14286 3.559097 4.767033
## 45.28571 3.624990 4.867807
## 45.42857 3.689083 4.965829
## 45.57143 3.751517 5.061313
## 45.71429 3.812413 5.154446
## 45.85714 3.871881 5.245394
## 46.00000 3.930016 5.334304
## 46.14286 3.986904 5.421306
## 46.28571 4.042622 5.506519
## 46.42857 4.097239 5.590049
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.21136693 0.08039574 0.14742085 0.11309509 0.21760838 0.57475349
##  [7] 0.08772841 0.16434936 0.14046243 0.15269996 0.14644515 0.12510382
## [13] 0.20512386 0.14325522
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -1.920834 -3.049553
## 44.71429 -2.292259 -3.548267
## 44.85714 -2.714001 -4.228747
## 45.00000 -3.035390 -4.702098
## 45.14286 -3.252861 -5.090016
## 45.28571 -3.161848 -5.139885
## 45.42857 -3.910763 -6.027436
## 45.57143 -4.225406 -6.549202
## 45.71429 -4.536207 -7.011886
## 45.85714 -4.828374 -7.465195
## 46.00000 -5.104751 -7.884566
## 46.14286 -5.391089 -8.311185
## 46.28571 -5.559925 -8.611757
## 46.42857 -5.862296 -9.041443
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 2.343568 3.472286
## 44.71429 2.453051 3.709059
## 44.85714 3.008843 4.523588
## 45.00000 3.261581 4.928288
## 45.14286 3.688077 5.525233
## 45.28571 4.311355 6.289392
## 45.42857 4.086220 6.202893
## 45.57143 4.554105 6.877901
## 45.71429 4.817131 7.292810
## 45.85714 5.133774 7.770595
## 46.00000 5.397641 8.177456
## 46.14286 5.641296 8.561392
## 46.28571 5.970172 9.022005
## 46.42857 6.148807 9.327954
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 14.04414 10.06199 11.15686 11.15686 11.15686 11.15686 11.15686 11.15686
##  [9] 11.15686 11.15686 11.15686 11.15686 11.15686 11.15686
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%        95%
## 44.57143 9.815161  7.5764717
## 44.71429 4.422008  1.4363803
## 44.85714 5.346684  2.2709613
## 45.00000 5.008774  1.7541730
## 45.14286 4.688493  1.2643452
## 45.28571 4.383339  0.7976527
## 45.42857 4.091352  0.3510975
## 45.57143 3.810962 -0.0777219
## 45.71429 3.540888 -0.4907645
## 45.85714 3.280069 -0.8896534
## 46.00000 3.027613 -1.2757508
## 46.14286 2.782765 -1.6502138
## 46.28571 2.544875 -2.0140346
## 46.42857 2.313383 -2.3680723
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 18.27313 20.51182
## 44.71429 15.70198 18.68760
## 44.85714 16.96704 20.04276
## 45.00000 17.30495 20.55955
## 45.14286 17.62523 21.04938
## 45.28571 17.93038 21.51607
## 45.42857 18.22237 21.96263
## 45.57143 18.50276 22.39144
## 45.71429 18.77283 22.80449
## 45.85714 19.03365 23.20338
## 46.00000 19.28611 23.58947
## 46.14286 19.53096 23.96394
## 46.28571 19.76885 24.32776
## 46.42857 20.00034 24.68179
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 0.4642651 0.5571550 0.6249265 0.4802255 0.4821609 0.4839931 0.4857274
##  [8] 0.4873690 0.4889231 0.4903942 0.4917867 0.4931049 0.4943527 0.4955338
## 
## $大分県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                 80%       95%
## 44.57143 -0.9102567 -1.637885
## 44.71429 -1.0255971 -1.863455
## 44.85714 -1.1257120 -2.052444
## 45.00000 -1.3345011 -2.295159
## 45.14286 -1.3420225 -2.307687
## 45.28571 -1.3486229 -2.318751
## 45.42857 -1.3544119 -2.328523
## 45.57143 -1.3594855 -2.337151
## 45.71429 -1.3639282 -2.344768
## 45.85714 -1.3678142 -2.351490
## 46.00000 -1.3712088 -2.357419
## 46.14286 -1.3741698 -2.362645
## 46.28571 -1.3767481 -2.367249
## 46.42857 -1.3789888 -2.371301
## 
## $大分県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 1.838787 2.566415
## 44.71429 2.139907 2.977765
## 44.85714 2.375565 3.302297
## 45.00000 2.294952 3.255610
## 45.14286 2.306344 3.272009
## 45.28571 2.316609 3.286737
## 45.42857 2.325867 3.299977
## 45.57143 2.334224 3.311889
## 45.71429 2.341774 3.322615
## 45.85714 2.348602 3.332279
## 46.00000 2.354782 3.340992
## 46.14286 2.360380 3.348855
## 46.28571 2.365453 3.355954
## 46.42857 2.370056 3.362369
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] -0.009549455 -0.006137241 -0.006137241 -0.006137241 -0.006137241
##  [6] -0.006137241 -0.006137241 -0.006137241 -0.006137241 -0.006137241
## [11] -0.006137241 -0.006137241 -0.006137241 -0.006137241
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -2.699903 -4.124090
## 44.71429 -2.810830 -4.295544
## 44.85714 -3.135768 -4.792495
## 45.00000 -3.430007 -5.242494
## 45.14286 -3.700887 -5.656770
## 45.28571 -3.953221 -6.042681
## 45.42857 -4.190365 -6.405361
## 45.57143 -4.414771 -6.748561
## 45.71429 -4.628295 -7.075118
## 45.85714 -4.832382 -7.387242
## 46.00000 -5.028182 -7.686692
## 46.14286 -5.216629 -7.974897
## 46.28571 -5.398495 -8.253036
## 46.42857 -5.574423 -8.522096
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 2.680804 4.104991
## 44.71429 2.798555 4.283270
## 44.85714 3.123494 4.780221
## 45.00000 3.417733 5.230220
## 45.14286 3.688613 5.644495
## 45.28571 3.940946 6.030406
## 45.42857 4.178091 6.393087
## 45.57143 4.402497 6.736287
## 45.71429 4.616021 7.062844
## 45.85714 4.820108 7.374967
## 46.00000 5.015907 7.674417
## 46.14286 5.204355 7.962622
## 46.28571 5.386220 8.240762
## 46.42857 5.562149 8.509821
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 1.885180 2.417082 2.736701 2.928760 3.044167 3.113515 3.155186 3.180226
##  [9] 3.195273 3.204314 3.209747 3.213012 3.214974 3.216152
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%       95%
## 44.57143 -2.097821 -4.206295
## 44.71429 -2.294388 -4.788489
## 44.85714 -2.250584 -4.890694
## 45.00000 -2.179712 -4.883974
## 45.14286 -2.124717 -4.860958
## 45.28571 -2.089705 -4.844124
## 45.42857 -2.070302 -4.836509
## 45.57143 -2.061548 -4.836375
## 45.71429 -2.059642 -4.841426
## 45.85714 -2.062010 -4.849833
## 46.00000 -2.066999 -4.860340
## 46.14286 -2.073580 -4.872133
## 46.28571 -2.081119 -4.884701
## 46.42857 -2.089230 -4.897730
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%       95%
## 44.57143 5.868181  7.976654
## 44.71429 7.128552  9.622654
## 44.85714 7.723987 10.364096
## 45.00000 8.037232 10.741493
## 45.14286 8.213051 10.949292
## 45.28571 8.316736 11.071154
## 45.42857 8.380675 11.146881
## 45.57143 8.422000 11.196828
## 45.71429 8.450187 11.231971
## 45.85714 8.470638 11.258462
## 46.00000 8.486494 11.279835
## 46.14286 8.499604 11.298156
## 46.28571 8.511066 11.314648
## 46.42857 8.521535 11.330035
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##  [1] 33.33647 31.26119 32.51608 35.27745 31.94474 37.98523 32.35039 33.90358
##  [9] 33.90358 33.90358 33.90358 33.90358 33.90358 33.90358
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##                80%        95%
## 44.57143 21.521544 15.2670984
## 44.71429 18.380581 11.5619961
## 44.85714 18.651459 11.3119700
## 45.00000 20.494167 12.6683696
## 45.14286 16.296639  8.0130339
## 45.28571 21.517660 12.8002564
## 45.42857 15.102248  5.9716334
## 45.57143 14.943076  4.9059930
## 45.71429 13.917014  3.3367679
## 45.85714 12.941116  1.8442607
## 46.00000 12.008673  0.4182115
## 46.14286 11.114349 -0.9495382
## 46.28571 10.253821 -2.2656030
## 46.42857  9.423523 -3.5354337
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 5) 
## End = c(46, 4) 
## Frequency = 7 
##               80%      95%
## 44.57143 45.15140 51.40585
## 44.71429 44.14181 50.96039
## 44.85714 46.38071 53.72020
## 45.00000 50.06073 57.88653
## 45.14286 47.59284 55.87645
## 45.28571 54.45279 63.17020
## 45.42857 49.59853 58.72915
## 45.57143 52.86408 62.90116
## 45.71429 53.89014 64.47038
## 45.85714 54.86603 65.96289
## 46.00000 55.79848 67.38894
## 46.14286 56.69280 68.75669
## 46.28571 57.55333 70.07275
## 46.42857 58.38363 71.34258